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Optimal Crowd-Powered Rating and Filtering Algorithms

Summary: Optimal crowd-powered filtering for data management; relaxes prior assumptions. Two approaches: a generalization yielding optimal but intractable solutions, and an efficient near-optimal strategy; achieves up to 30% error reduction in peer evaluation. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
10945
Venue
VLDB
Year
2014
Pagerank
6.149053e-05
Overall Rank
4,479 | 68.85%
DOI
-

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Showing 7 of 7 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
94 CrowdDB: Answering Queries with Crowdsourcing 2011 SIGMOD 0.00051013264
249 Crowdsourced Databases: Query Processing with People 2011 CIDR 0.00030740523
263 CrowdER: Crowdsourcing Entity Resolution 2012 VLDB 0.00029862413
267 Human-powered Sorts and Joins 2012 VLDB 0.00029690405
859 So Who Won? Dynamic Max Discovery with the Crowd 2012 SIGMOD 0.00015870894
1,164 CrowdScreen: Algorithms for Filtering Data with Humans 2012 SIGMOD 0.00013564823
3,100 Crowd Mining 2013 SIGMOD 7.5634778e-05
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